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An accidental discovery could have future implications for the material makeup for MRI contrast media.

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The California physician may not face prison time after all, if a judge grants his defense team’s request that he be admitted to a mental health diversion program. 

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The new technique uncovers hidden inflammation in patients who, despite undergoing extensive treatment for the condition, had worsening symptoms.

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Finding such discrepancies is critical to the continuity of patient care, as medical records and reports are often utilized across multiple providers and facilities. 

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Johns Hopkins researchers demonstrated the worthiness of the concept this year.

The imaging iodine contrast shortage is delaying procedures and causing rationing at hospitals. impact is it having on hospitals and the tough decisions that are being made to triage patients to determine if they will get a contrast CT scan or an interventional or surgical procedure requiring contrast. Photo by Dave Fornell

This could be especially helpful when timely clinical decisions relative to the use of a contrast agent need to be made.

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With the earth’s warmest March on record in 175 years barely in the rearview mirror, a team of radiology experts says medical imaging must step up its sustainability efforts. 

Advanced artificial intelligence (AI) models can evaluate cardiovascular risk in routine chest CT scans without contrast, according to new research published in Nature Communications.[1] In fact, the authors noted, the AI approach may be more effective at identifying issues than relying on guidance from radiologists. Representative non-contrast CT slices for two patients (left), with super-imposed segmentations (right). One artificial intelligence (AI) model was used to segment a cardiac mask.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

Example of a radiology diagnostic aid artificial intelligence (AI) algorithm with Lunit's mammography cancer lesion detection system.

Keith J. Dreyer, DO, American College of Radiology (ACR) Data Science Institute chief science officer, breaks down radiology AI down into 4 areas and discusses where these areas stand with regulatory approval.

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Prior attempts at imaging large fossils such as mammoth tusks failed to capture the full artifact with just one scan, instead requiring multiple partial scans that were subsequently pieced together.

“Although regularly assessing and updating these models is necessary to ensure accurate performance, there is no standard approach to addressing model drift.” 

Example of an artificial intelligence (AI) app store on the Sectra website, where Sectra PACS users can select the AI algorithms they want that are already integrated into the Sectra System. Other vendors have followed a similar approach to AI developed by many smaller vendors they partner with.

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains how radiology vendors have developed AI app stores to make it easier to access new FDA cleared AI algorithms.
 

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Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

Advanced imaging equipment using ionizing radiation enters the global market at the rate of one new technology every five years.

Raquel Roman, chair of the Radiology Business Management Association (RBMA) Young Professionals Committee, and director of growth at Essential Radiology, explains how the group mentors the next generation leaders.